用多变量处理和重叠密度热图可视化两种方法表征光谱和其它分析数据

会议Poster

Characterization of Spectra and Other Analytical Data via Combination of Two Methods: Multivariate Processing and Overlap Density Heatmap Visualization

Abstract

The use of methods such as Principal Component Analysis (PCA) to perform multivariate analyses on spectral and chromatographic data has used for years in the field of analytical chemistry.  In this study, we will introduce a new method that combines a second patent pending technology known as Overlap Density Heatmap (ODH). ODH allows the user to explore data similarities and dissimilarities in large databases by providing information about the most and/or least commonly occurring spectral or chromatographic features in a data set(s).  In this session, the combination of these two approaches for spectroscopic analysis will be explored through a series of successful applications and case studies.

Conclusions

Principal Component Analysis (PCA) appears to be a valuable tool to analyze the results of standard spectral searches a spectral query and hit list-providing useful insights into the nature of the compounds in the hit list relative to the query. Overlap Density Heatmaps (ODHs) not only confirm the value of the technique, but are also a useful complement to the multivariate processing capabilities afforded by PCA.  This technique is an excellent tool to identify components in mixtures and can be used effectively in the polymer industry to analyze polymeric samples. It is more precise than spectral subtraction, which performs the point-by-point subtraction of one spectrum from another, and it is especially useful when analyzing mixtures or composite spectra.